Published by the Foundation for Open Access Statistics
Editors-in-chief: Bettina GrĂ¼n, Edzer Pebesma & Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
lgcp: An R Package for Inference with Spatial and Spatio-Temporal Log-Gaussian Cox Processes | Taylor | Journal of Statistical Software
Authors: Benjamin M. Taylor, Tilman M. Davies, Barry S. Rowlingson, Peter J. Diggle
Title: lgcp: An R Package for Inference with Spatial and Spatio-Temporal Log-Gaussian Cox Processes
Abstract: This paper introduces an R package for spatial and spatio-temporal prediction and forecasting for log-Gaussian Cox processes. The main computational tool for these models is Markov chain Monte Carlo (MCMC) and the new package, lgcp, therefore also provides an extensible suite of functions for implementing MCMC algorithms for processes of this type. The modeling framework and details of inferential procedures are first presented before a tour of lgcp functionality is given via a walk-through data-analysis. Topics covered include reading in and converting data, estimation of the key components and parameters of the model, specifying output and simulation quantities, computation of Monte Carlo expectations, post-processing and simulation of data sets.

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Paper: lgcp: An R Package for Inference with Spatial and Spatio-Temporal Log-Gaussian Cox Processes     Download PDF (Downloads: 2103)
Supplements:
lgcp_1.01.tar.gz: R source package Download (Downloads: 483; 1MB)
v52i04.R: R example code from the paper Download (Downloads: 480; 5KB)
v52i04.rda: R binary data for replication of examples Download (Downloads: 475; 378KB)
GBR_adm1.RData: Supplementary data Download (Downloads: 736; 2MB)
GBR_adm2.RData: Supplementary data Download (Downloads: 600; 1MB)

DOI: 10.18637/jss.v052.i04

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.